Challenges and potential of the Semantic Web for tourism

The paper explores tourism challenges and potential of the Semantic Web from a theoretical and industry perspective. It first examines tourism business networks and explores a main theme of network interoperability - data standards- followed by technology deficiencies of Web 1.0 and 2.0 and Semantic Web solutions. It then explicates Semantic opportunities and challenges for tourism, including an industry perspective through a qualitative approach. Industry leaders considered that the new Web era was imminent and heralded benefits for supply and demand side interoperability, although management and technical challenges could impede progress and delay realisation

Sistem Promosi Pariwisata Menggunakan Ontologi

Pariwisata merupakan sektor yang penting di Indonesia. World Tourism Organization (WTO) meramalkan pada tahun  2019,  bahwa  industri pariwisata Asia Pasifik akan mengalami perkembangan yang menjanjikan terutama dari segi pendapatan. Sistem  promosi pariwisata berbasis konteks yang ada hanya mengakomodasi pelancong yang sudah memiliki rencana dengan jelas (pelancong terencana), sedangkan pelancong yang sekedar ingin menjelajahi kota, berjalan-jalan  atau menghabiskan waktu luang (pelancong dadakan) belum ada yang mengakomodasi. Salah satu solusi tersebut adalah dengan menggunakan teknologi piranti bergerak dan ontologi.  Piranti bergerak memudahkan pelancong untuk mendapatkan informasi kapanpun dan dimanapun. Sedangkan penggunaan ontologi akan mempermudah penyajian informasi yang lebih relevan kepada pelancong. Ontologi dalam konteks studi ini adalah ontologi probabilitas dengan pendekatan bayesian network. Pengujian sistem dibagi menjadi dua bagian yaitu uji validitas kebutuhan sistem dengan menggunakan perkaka Requirements Traceability Matrixs (RTM) dan pengujian sistem purwarupa dengan pengujian kotak hitam. Secara umum, fungsionalitas sistem berjalan baik dan sesuai dengan rancangan sistem

Testing for Collinearity using Bayesian Analysis

Testing for collinearity continues to be a controversial issue in the literature. Multicollinearity detection criteria, such as the variance inflation factor, often fail to detect the true extent of multicollinearity. In this article, we propose utilizing the Bayesian approach as an attractive alternative. Under the Bayesian approach, we recommend comparing the marginal posterior of regression parameters under two different priors. If the difference in the posterior under these two priors is pronounced, one can surmise that collinearity is harmful. The Kolmogorov–Smirnov test can also be used as further evidence to confirm whether the posterior difference is significant

Diagnosing and correcting the effects of multicollinearity:Bayesian implications of ridge regression

When faced with the problem of multicollinearity most tourism researchers recommend mean-centering the variables. This procedure however does not work. It is actually one of the biggest misconceptions we have in the field. We propose instead using Bayesian ridge regression and treat the biasing constant as a parameter about which inferences are to be made. It is well known that many estimates of the biasing constant have been proposed in the literature. When the coefficients in ridge regression have a conjugate prior distribution, formal selection can be based on the marginal likelihood. In the non-conjugate case, we propose a conditionally conjugate prior for the biasing constant, and show that Gibbs sampling can be employed to make inferences about ridge regression parameters as well as the biasing constant itself. We examine posterior sensitivity and apply the techniques to a tourism data set

توسعه یک سامانه برنامه ریزی گردشگری بر پایه مدیریت زمانی و مکانی

امروزه گردشگری به عنوان یکی از منابع اقتصادی، به خصوص در کشورهای دارای سابقه فرهنگی و جاذبه‌های متعدد گردشگری مورد توجه قرار گرفته است. در این راستا سامانه‌های توصیه گر گردشگری جهت کمک به گردشگران طراحی شده است. برنامه‌ریزی گردشگری قبل از اقدام به سفر، به شخص امکان بازدید بهتر و تعداد بیشتری از مکان‌ها را می‌دهد. در نتیجه انجام آن امری ضروری و اجتناب ناپذیر می‌باشد. در برنامه‌ریزی گردشگری عوامل متعدد و متنوعی دخیل هستند. مدیریت زمانی و انتخاب جاذبه‌های گردشگری مطابق با علایق شخص از مهمترین این عوامل می‌باشد. در مقاله حاضر یک سامانه برنامه‌ریزی گردشگری مبتنی بر وب جهت کمک به گردشگران برای بازدید از جاذبه‌های مورد علاقه در حداقل زمان ممکن، طراحی و پیاده سازی گردید. در توسعه این سامانه از تلفیق روش‌‌های مبتنی بر سامانه حامی تصمیم‌گیری مکانی و توابع تحلیل مکانی استفاده شده است. معیارهای در نظر گرفته شده در آن شامل معیارهای مربوط به ژئوتوریسم و عوامل مؤثر زمین شناختی و سایر گونه‌های گردشگری می‌باشد. سامانه طراحی شده از طریق اطلاعاتی از قبیل علایق، تعداد روزها و مکان شروع حرکت گردشگر، برای هر روز به صورت جداگانه، برنامه‌ریزی گردشگری را انجام داده و همراه با ارائه طرح روزانه گردشگر، بهترین مسیر بین مکان‌های منتخب را تعیین می‌نماید و مدیریت زمانی و مکانی به صورت همزمان انجام می‌دهد. این سامانه برای مکان‌های گردشگری استان تهران، پیاده سازی و قابلیت‌های آن مورد ارزیابی قرار گرفت

Recommender systems in model-driven engineering: A systematic mapping review

Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of researchThis work has been funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 813884 (Lowcomote [134]), by the Spanish Ministry of Science (projects MASSIVE, RTI2018-095255-B-I00, and FIT, PID2019-108965GB-I00) and by the R&D programme of Madrid (Project FORTE, P2018/TCS-431

A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented

Hierarchical categorisation of tags for delicious

In the scenario of social bookmarking, a user browsing the Web bookmarks web pages and assigns free-text labels (i.e., tags) to them according to their personal preferences. In this technical report, we approach one of the practical aspects when it comes to represent users' interests from their tagging activity, namely the categorization of tags into high-level categories of interest. The reason is that the representation of user profiles on the basis of the myriad of tags available on the Web is certainly unfeasible from various practical perspectives; mainly concerning the unavailability of data to reliably, accurately measure interests across such fine-grained categorisation, and, should the data be available, its overwhelming computational intractability. Motivated by this, our study presents the results of a categorization process whereby a collection of tags posted at Delicious #http://delicious.com# are classified into 200 subcategories of interest.Preprin